Research Article
Exploring K-12 teachers’ attitudes and perceptions towards the use of AI applications in the teaching process
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1 Department of Psychology, School of Social Sciences, Arts and Humanities, Neapolis University Pafos, Pafos, CYPRUS* Corresponding Author
Contemporary Educational Technology, 18(2), April 2026, ep645, https://doi.org/10.30935/cedtech/18149
Published: 17 March 2026
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ABSTRACT
Artificial intelligence (AI) is rapidly reshaping instructional processes by providing new opportunities for personalized learning and enhanced classroom support. Although its potential is widely acknowledged, further investigation is needed into K-12 teachers’ attitudes and perceptions towards integrating AI applications into educational practice, as well as the factors that shape these attitudes and influence their intention to adopt such tools. Addressing these issues, the present study explores the integration of AI in education by focusing on educators’ perspectives. Data from 494 educators collected via a technology acceptance model-based questionnaire, assessed attitudes, perceived usefulness (PU), ease of use, and behavioral intention. Data analysis indicates that teachers’ attitudes and perceptions are significantly influenced by various demographic variables—including gender, academic qualifications, subject area, age, and teaching experience—as well as school-related factors such as institutional context and engagement with information and communication technologies (ICT). The results show generally positive attitude towards AI, but a neutral stance in terms of its ease of use, suggesting a gap between general acceptance and practical readiness. Four key predictors of teachers’ intention to integrate AI into their educational practices were identified: (a) PU, (b) positive attitude, (c) perceived ease of use, and (d) ICT training. These findings underscore the importance of enhancing teachers’ digital competencies and providing targeted professional development opportunities to facilitate the effective adoption of AI within the K-12 educational context.
CITATION (APA)
Karagiorgou, C.-E., Koukis, N., Koundourou, C., & Tzovla, E. (2026). Exploring K-12 teachers’ attitudes and perceptions towards the use of AI applications in the teaching process. Contemporary Educational Technology, 18(2), ep645. https://doi.org/10.30935/cedtech/18149
REFERENCES
- Aburbeian, A. M., Owda, A. Y., & Owda, M. (2022). A technology acceptance model survey of the metaverse prospects. AI, 3(2), 285-302. https://doi.org/10.3390/ai3020018
- Akgün, S., & Greenhow, C. (2021). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI And Ethics, 2(3), 431-440. https://doi.org/10.1007/s43681-021-00096-7
- Al Darayseh, A. S. (2023). Acceptance of artificial intelligence in teaching science: Science teachers’ perspective. Computers and Education: Artificial Intelligence, 4, Article 100132. https://doi.org/10.1016/j.caeai.2023.100132
- Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology acceptance model in M-learning context: A systematic review. Computers & Education, 125, 389-412. https://doi.org/10.1016/j.compedu.2018.06.008
- Alwaqdani, M. (2024). Investigating teachers’ perceptions of artificial intelligence tools in education: Potential and difficulties. Education and Information Technologies, 30, 2737-2755. https://doi.org/10.1007/s10639-024-12903-9
- Antonenko, P., & Abramowitz, B. (2022). In-service teachers’ (mis) conceptions of artificial intelligence in K-12 science education. Journal of Research on Technology in Education, 55(1), 64-78. https://doi.org/10.1080/15391523.2022.2119450
- Asiri, M., & El Aasar, S. (2022). Employing technology acceptance model to assess the reality of using augmented reality applications in teaching from teachers’ point of view in Najran. Journal of Positive School Psychology, 6(2), 5241-5255.
- Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K., & Oyelere, S. S. (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers & Education: Artificial Intelligence, 3, Article 100099. https://doi.org/10.1016/j.caeai.2022.100099
- Chiu, T. K., & Chai, C. (2020). Sustainable curriculum planning for artificial intelligence education: A self-determination theory perspective. Sustainability, 12(14), Article 5568. https://doi.org/10.3390/su12145568
- Chou, C., Shen, T., Shen, T., & Shen, C. (2024). Teachers’ adoption of AI-supported teaching behavior and its influencing factors: Using structural equation modeling. Journal of Computers in Education, 12, 853-896. https://doi.org/10.1007/s40692-024-00332-z
- Chounta, I., Bardone, E., Raudsep, A., & Pedaste, M. (2021). Exploring teachers’ perceptions of artificial intelligence as a tool to support their practice in Estonian K-12 education. International Journal of Artificial Intelligence in Education, 32(3), 725-755. https://doi.org/10.1007/s40593-021-00243-5
- Colchester, K., Hagras, H., Alghazzawi, D., & Aldabbagh, G. (2016). A survey of artificial intelligence techniques employed for adaptive educational systems within e-learning platforms. Journal of Artificial Intelligence and Soft Computing Research, 7(1), 47-64. https://doi.org/10.1515/jaiscr-2017-0004
- Durak, H. (2019). Examining the acceptance and use of online social networks by preservice teachers within the context of unified theory of acceptance and use of technology model. Journal of Computing in Higher Education, 31(1), 173-209. https://doi.org/10.1007/s12528-018-9200-6
- Field, A. (2017). Discovering statistics using IBM SPSS (5th ed.). SAGE.
- Fokidis, E. (2017). Exploring preservice teachers’ early views on the educational uses of multi-user three-dimensional virtual environments. e-Journal of Science & Technology, 12(1).
- Galindo-Domínguez, H., Delgado, N., Campo, L., & Losada, D. (2024). Relationship between teachers’ digital competence and attitudes towards artificial intelligence in education. International Journal of Educational Research, 126, Article 102381. https://doi.org/10.1016/j.ijer.2024.102381
- Guner, H., & Acarturk, C. (2020). The use and acceptance of ICT by senior citizens: A comparison of technology acceptance model (TAM) for elderly and young adults. Universal Access in the Information Society, 19(2), 311-330. https://doi.org/10.1007/s10209-018-0642-4
- Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2021). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32(3), 504-526. https://doi.org/10.1007/s40593-021-00239-1
- Jimoyiannis, A., & Koukis, N. (2023). Exploring teachers’ readiness and beliefs about emergency remote teaching in the midst of the COVID-19 pandemic. Technology, Pedagogy and Education, 32(2), 205-222. https://doi.org/10.1080/1475939X.2022.2163421
- Krutka, D. G., Manca, S., Galvin, S. M., Greenhow, C., Koehler, M. J., & Askari, E. (2019). Teaching “Against” social media: Confronting problems of profit in the curriculum. Teachers College Record the Voice of Scholarship in Education, 121(14), 1-42. https://doi.org/10.1177/016146811912101410
- Mogavi, R. H., Deng, C., Kim, J. J., Zhou, P., Kwon, Y. D., Metwally, A. H. S., Tlili, A., Bassanelli, S., Bucchiarone, A., Gujar, S., Nacke, L. E., & Hui, P. (2024). ChatGPT in education: A blessing or a curse? A qualitative study exploring early adopters’ utilization and perceptions. Computers in Human Behavior Artificial Humans, 2(1), Article 100027. https://doi.org/10.1016/j.chbah.2023.100027
- Pokhrel, S., & Chhetri, R. (2021). A literature review on impact of COVID-19 pandemic on teaching and learning. Higher Education for the Future, 8(1), 133-141. https://doi.org/10.1177/2347631120983481
- Polak, S., Schiavo, G., & Zancanaro, M. (2022). Teachers’ perspective on artificial intelligence education: An initial investigation. In Proceedings of the CHI Conference on Human Factors in Computing Systems Extended Abstracts. https://doi.org/10.1145/3491101.3519866
- Razali, N. and Wah, Y. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. Journal of Statistical Modeling and Analytics, 2, 21-33.
- Regan, P. M., & Jesse, J. (2018). Ethical challenges of edtech, big data and personalized learning: Twenty-first century student sorting and tracking. Ethics and Information Technology, 21(3), 167-179. https://doi.org/10.1007/s10676-018-9492-2
- Saadé, R. G., Nebebe, F., & Tan, W. (2007). Viability of the “Technology Acceptance Model” in multimedia learning environments: A comparative study. Interdisciplinary Journal of e-Skills and Lifelong Learning, 3, 175-184. https://doi.org/10.28945/392
- Sanusi, I. T., Ayanwale, M. A., & Tolorunleke, A. E. (2024). Investigating pre-service teachers’ artificial intelligence perception from the perspective of planned behavior theory. Computers & Education: Artificial Intelligence, 6, Article 100202. https://doi.org/10.1016/j.caeai.2024.100202
- Scherer, R., Siddiq, F., & Tondeur, J. (2018). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13-35. https://doi.org/10.1016/j.compedu.2018.09.009
- Stahl, B. C., & Wright, D. (2018). Ethics and privacy in AI and big data: Implementing responsible research and innovation. IEEE Security & Privacy, 16(3), 26-33. https://doi.org/10.1109/msp.2018.2701164
- Wang, Y., Liu, C., & Tu, Y.-F. (2021). Factors affecting the adoption of AI-Based applications in higher education: An analysis of teachers’ perspectives using structural equation modeling. Educational Technology & Society, 24(3), 116-129.
- Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023). Acceptance of artificial intelligence among pre-service teachers: A multigroup analysis. International Journal of Educational Technology in Higher Education, 20, Article 49. https://doi.org/10.1186/s41239-023-00420-7
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